Tables

Descriptive tables

Global differences by:, age, sex, world region

IHME WHO Dif % difference (WHO / IHME) % difference (IHME / WHO)
Total HIV+TB only 211604 389042 177438.13321 83.85% -45.61%
TB only 1111312 1379440 268128.44955 24.13% -19.44%
Total TB 1322916 1768482 445566.58275 33.68% -25.19%
Adults HIV+TB only 177567 348026 170458.90473 96% -48.98%
TB only 1075691 1210620 134929.12946 12.54% -11.15%
Total TB 1253257 1558645 305388.03419 24.37% -19.59%
Children HIV+TB only 34037 41016 6979.22848 20.5% -17.02%
TB only 35621 168821 133199.32009 373.93% -78.9%
Total TB 69659 209837 140178.54857 201.24% -66.8%
Female HIV+TB only 78110 143496 65386.51804 83.71% -45.57%
TB only 367764 352488 15276.43876 -4.15% 4.33%
Total TB 445874 495984 50110.07929 11.24% -10.1%
Male HIV+TB only 99457 204471 105013.90757 105.59% -51.36%
TB only 707927 858132 150205.56821 21.22% -17.5%
Total TB 807383 1062603 255219.47578 31.61% -24.02%
AMR HIV+TB only 579 620 41.31917 7.14% -6.66%
TB only 2036 1914 122.37010 -6.01% 6.39%
Total TB 2615 2534 81.05093 -3.1% 3.2%
EMR HIV+TB only 165 533 368.30203 223.05% -69.04%
TB only 14658 14572 85.51575 -0.58% 0.59%
Total TB 14823 15106 282.78629 1.91% -1.87%
EUR HIV+TB only 212 374 161.68749 76.15% -43.23%
TB only 2383 2999 615.93832 25.85% -20.54%
Total TB 2595 3373 777.62581 29.97% -23.06%
SEA HIV+TB only 19310 28870 9560.04060 49.51% -33.11%
TB only 333250 345889 12639.43214 3.79% -3.65%
Total TB 352560 374759 22199.47275 6.3% -5.92%
WPR HIV+TB only 2057 2010 47.13348 -2.29% 2.35%
TB only 39055 28351 10704.20283 -27.41% 37.76%
Total TB 41112 30361 10751.33632 -26.15% 35.41%

Analytical tables

Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.

This section is unfinished.

Graphs

Descriptive graphs

Standardized difference

Rankings of highest absolute and standardized differences for IHME and WHO.

Standardized difference for incidence

AB metric (a-b) / (a+b)

Rankings of highest absolute and standardized differences for IHME and WHO.

Maps

The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.

Analytical for adjusted_stand_dif

In the following four charts, Libya has been excluded as an outlier.

a) HIV prevalence among TB cases

b) Rifampicine resistance (MDR prevalence)

b.i) Rifampicine resistance (MDR prevalence using reported cases)

c) CDR (case detection rate per WHO)

c.i) CDR (case detection rate per IHME)

c.ii) Correlation between different case detections rates

d) CFR (case fatality rate)

Prevalence survey’s effect

Association of prevalence survey and standardized difference

Analytical for stand_dif_inc_adj

a) HIV prevalence among TB cases

b) Rifampicine resistance (MDR prevalence)

b.i) Rifampicine resistance (MDR prevalence using reported cases)

c) CDR (case detection rate per WHO)

c.i) CDR (case detection rate per IHME)

d) CFR (case fatality rate)

Prevalence survey’s effect

Association of prevalence survey and standardized difference

Analytical for adjusted_ab

a) HIV prevalence among TB cases

b) Rifampicine resistance (MDR prevalence)

b.i) Rifampicine resistance (MDR prevalence using reported cases)

c) CDR (case detection rate per WHO)

c.i) CDR (case detection rate per IHME)

d) CFR (case fatality rate)

Prevalence survey’s effect

Association of prevalence survey and standardized difference

Modeling

Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.

95% confidence intervals

Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.

95% confidence intervals

Interactive map

Relative difference (function of maximum estimate)

(Unfinished)

Map of world

Map of Mozambique

Correlation coefficients

Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.

cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')
[1] 0.2277724
cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')
[1] -0.3776421
cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')
[1] 0.01413998
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')
[1] -0.04647787
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')
[1] -0.0384391
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')
[1] -0.05203254
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')
[1] -0.04351185
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')
[1] -0.03215424
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')
[1] -0.04752176
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')
[1] -0.0491367
cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')
[1] -0.009870141
cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')
[1] -0.02881747

Hypothesis testing on prevalence survey

Does region affect likelihood of having a prevalence survey?

xt <- table(df$prevsurvey, df$who_region)
xt
   
    AFR AMR EMR EUR SEA WPR
  0  37  37  20  52   8  22
  1  10   0   2   0   3   4
chisq.test(xt)

    Pearson's Chi-squared test

data:  xt
X-squared = 21.511, df = 5, p-value = 0.0006482

Does having a prev survey affect the adjusted stand diff?

t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
       y = df$adjusted_stand_dif[df$prevsurvey == 1])

    Welch Two Sample t-test

data:  df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -1.1254, df = 18.051, p-value = 0.2751
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -17.370292   5.250238
sample estimates:
mean of x mean of y 
0.4643521 6.5243789